Self-driving road vehicles rely on an array of sensors and a roadway model representing features of the roadway on which the self-driving road vehicle is travelling. The roadway model is derived from survey data of the roadways (e.g., point clouds, georeferenced images) acquired on an earlier date. The control system, typically incorporating an onboard computer, uses the sensors to obtain information about the environment, which can then be used in conjunction with the roadway model to direct the self-driving road vehicle along the roadway while complying with traffic signals and avoiding collisions with pedestrians and other vehicles. Self-driving road vehicles can navigate autonomously on a roadway without human intervention.
In order for a self-driving road vehicle to operate effectively, the roadway model upon which it relies must be sufficiently up to date. For example, if the position of a highway on-ramp or off-ramp on the real-world roadway has changed but the roadway model does not reflect this change, a self-driving road vehicle may be unable to navigate from or to the ramp. Even less significant changes or discrepancies between the roadway and the roadway model, such as fading or repainting of lane markings, can impede the performance of self-driving road vehicles.
Thus, it is important that the roadway model be kept up to date. At the same time, however, updating a roadway model involves traversing the corresponding roadway with a sensor-laden vehicle, followed by processing of the sensor data so obtained. Moreover, because it is not necessarily known which portions of the roadway have changed so as to require updating of the corresponding portions of the roadway model, the entire roadway must be traversed. This can be a very expensive process for a roadway that comprises an extensive network of roads. Thus, the cost of updating the roadway model may deter the custodian of the roadway model from keeping the roadway model as up to date as possible.